Author: Everloop Team

  • How Forums Like Reddit Are Changing SEO

    How Forums Like Reddit Are Changing SEO

    Why people’s online conversations are outranking generic blog posts in search.

    If you’ve ever tried to get a real answer to a problem over the Internet, chances are you tacked on “reddit” to the end of your query.

    Reddit is a well-known community forum, around since 2005. It’s best known for those expert answers, a place to either ask your question or to find the answers to questions you’ve never had to (or wanted to) ask. It’s a been a longtime rabbit hole of the Internet.

    Sites like Reddit and Quora have also become unexpected goldmines for AI search engines like ChatGPT and Gemini. 

    Why is AI using Reddit as a resource?

    Search engines, and increasingly generative AI search tools, are relying heavily on community-sourced content for nuanced, authentic, and context-rich answers. Reddit has a neatly organized structure for information gathering and link crawling. It is also timely, a real-time feed for people to post and upvote crowd-sourced answers. 

    Reddit has made deals with certain AI search engines, like OpenAI, for the right price. Jay Peters from the Verge stated that Reddit “sued Anthropic in June, claiming Anthropic was still scraping from Reddit even after Anthropic said it wasn’t scraping anymore”. 

    Training models and AI companies alike feed off of this archival data. For people in marketing, this shift is both a threat and an opportunity.

    Why forums are winning AI search

    1. Fresh, High-Volume Content
     Communities like Reddit and Quora churn out millions of posts and comments daily. Search algorithms—and AI models—love fresh data, and these platforms provide a constant stream of it.

    2. Depth and Diversity of Perspectives
     A branded blog post might give one point of view, but a forum thread delivers dozens. AI systems often pull from multiple perspectives when generating summaries, making community threads a rich source of training material.

    3. Trust and Authenticity Signals
     Usernames, upvotes, timestamps—these are all credibility markers. Even if the post isn’t perfect, it’s harder to fake the collective voice of a community than a polished single-author piece.

    Posting interesting, relevant content

    Historically, SEO was about bringing people to your site. Now, it’s about bringing the brand into the spaces where the conversations already are. And here’s the key: if you treat these spaces as dumping grounds for links, you’ll be ignored or banned.

    Instead, think of yourself (or your brand voice) as a community member first, marketer second. Instead of optimizing blog posts or content for search engine optimization, companies should post nuanced, well-researched pieces that invite conversation and discourse on these forums. 

    • Find relevant threads before they become popular, or create an expert presence in the threads your audience frequents
    • Lead with value and not your links. Links can feel inauthentic and spam-like.
    • You can use the conversation generated to structure it into a blog post. Creating interesting discussion will allow you to create content people actually want to read.

    You can create contextual advertising for your brand just by offering advice where it’s wanted.

    The Bottom Line

    As generative search becomes the norm, expect even more Reddit and Quora content to be quoted, summarized, and surfaced as “the internet’s consensus” on niche topics. Being part of these conversations today means your words could be echoed tomorrow in AI-driven search answers.

    The brands that win in this space will treat community participation as a core channel, not a side project. The sooner you start speaking in the language of the internet’s most trusted forums, the more likely you are to be part of the answers people see, even if they never visit your URL.

  • Let AI Handle Automation. Focus On The Vision

    Let AI Handle Automation. Focus On The Vision

    Most marketing teams spend hours every week pulling data from different platforms and trying to make sense of it all.

    Some teams have found a better way.

    How Your Tools Shape Your Thinking

    There’s an interesting pattern in how we work: the tools we choose quietly influence how we think. A marketer with a powerful desktop setup naturally tackles bigger, more complex projects than working on a tablet. The apps we download guide us toward certain ways of solving problems and unlock or restrict our perceived capabilities.

    The successful teams? They were using AI to analyze customer videos, social media posts, and shopping patterns while their competitors were still manually tracking basic metrics like email clicks.

    This gave them insights about customer behavior that would have taken months to discover the old way, and allow them to save time to prioritize strategic tasks.

    The Smart Way to Use AI in Marketing

    Right now, AI can do something remarkable: it can process huge amounts of messy data—images, videos, social conversations—and find patterns that help us understand what customers really want.

    This isn’t about replacing human creativity. It’s about using AI to handle the heavy analytical work so marketers can focus on what they do best: coming up with great ideas and building genuine connections with people.

    Think of it this way: AI finds the insights, humans create the magic.

    Two Different Approaches

    We’re seeing marketers split into two groups:

    The basic approach: Using AI for simple tasks like posting on social media or sending follow-up emails.

    The strategic approach: Using AI to discover market trends early, predict what customers will want next, and personalize experiences—then using human creativity to build campaigns that truly stand out.

    The strategic group isn’t just getting better results. They’re discovering opportunities that their competitors don’t even know exist yet.

    Why This Matters Now

    Companies that understand this shift are building a real advantage. They’re teaching their teams how to work with AI, creating clear plans for using it effectively, and giving their best strategic thinkers the space to focus on big-picture thinking.

    Meanwhile, AI handles the data analysis and pattern recognition that used to eat up so much time.

    The Bigger Picture

    The most successful marketers moving forward will be those who combine AI’s analytical power with human creativity and strategic thinking. They’ll use technology to uncover insights while relying on their own originality to create campaigns that resonate with real people.

    The Bottom Line

     AI doesn’t replace good marketing—it makes good marketing possible at a scale and speed we’ve never seen before.

    The teams that figure out this balance first are the ones writing the playbook that everyone else will eventually follow. They’re not just keeping up with change—they’re creating it.

    The question for every marketer today is simple: Will you use AI to amplify your best human skills, or will you let this opportunity pass by while others reshape the landscape?

  • How Multi‑Variant AI Optimization Works (Beyond A/B)

    How Multi‑Variant AI Optimization Works (Beyond A/B)

    Why settle for A/B when you can explore A–Z?

    Most digital marketers are familiar with A/B testing: test two versions of a page, measure performance, and pick a winner. It’s simple, intuitive—and deeply limiting.

    Today, with AI-native platforms, we’re entering a new era of optimization that moves beyond two-version testing. Multi-variant AI optimization allows users to test multiple designs at the same time like modifying headlines, CTAs, or images to find the most effective combinations. This enables websites to evolve continuously, responding to each visitor, campaign, or even time of day—without human micromanagement.

    Here’s how it works.

    The Limits of Traditional A/B Testing

    A/B testing has powered the CRO industry for over a decade. But it comes with real trade-offs:

    • Slow cycles: You need significant traffic and time to reach statistical confidence.
    • Small scope: You’re often testing just one variable at a time—like a headline or CTA.
    • One-size-fits-all: Everyone gets the same “winning” version, even if it’s not best for them.

    This made sense when tools and teams were manual. But today, it’s like navigating with a compass when you have GPS available.

    Enter: Multi‑Variant AI Optimization

    Instead of pitting two static versions against each other, AI systems can test and adapt thousands of micro-variations in real-time. These aren’t just randomized tests—they’re intelligently generated, targeted, and optimized based on live performance data.

    At a high level, here’s how the process works:

    1. Generate Multiple Variants—Not Just Two

    AI starts with your base content—say, a landing page. It generates multiple variations by adjusting:

    • Headlines, CTAs, product descriptions
    • Layout, structure, even image selection
    • Tone, urgency, or length of messaging

    These variants aren’t random. They’re shaped by training data and design principles to remain on-brand and goal-aligned.

    2. Serve Variants Dynamically Based on Context

    Unlike static tests, AI-driven systems can serve different versions based on real-time factors:

    • Ad campaign source or keyword
    • User’s location, device, or time of day
    • Behavioral signals or past site interactions

    This means visitors see the version most likely to resonate with them—before a single test completes.

    3. Learn in Real-Time, Not Just Post-Test

    Instead of waiting weeks to declare a winner, multi-variant systems adjust continuously. Using techniques like multi-armed bandits and reinforcement learning, they:

    • Shift traffic toward high-performing variants
    • Explore new possibilities based on partial successes
    • Optimize for downstream goals (e.g. revenue, not just clicks)

    It’s not just “test and learn”—it’s “test while learning.”

    4. Evolve the Experience Over Time

    What worked last month may not work today. With multi-variant AI optimization:

    • Winning experiences evolve with your audience
    • Seasonal or campaign-specific variants surface automatically
    • You avoid the dreaded “set it and forget it” CRO trap

    This is what makes websites feel alive—constantly tuning to the user and the moment.

    Why This Matters Now

    We’re in a world of dynamic attention, fragmented channels, and high CACs. Optimization can’t be a once-a-quarter initiative. It has to be built-in, always-on, and adaptive by design.

    Multi-variant AI optimization makes that possible—not just for enterprise growth teams, but for any brand that wants more from their traffic.

    The Bottom Line

    If A/B testing was the calculator of conversion optimization, multi-variant AI is the modern computer. It’s faster, smarter, and always learning.

    And in a world where every click counts, that difference can be transformative.

  • The Agentic Web Is Already Here

    The Agentic Web Is Already Here

    AI agents are changing how people discover, evaluate, and act online. The way we design for the internet must evolve with them.

    Over the past three decades, the internet has gone through a series of tectonic shifts—from static pages to dynamic content, from desktop to mobile, from keyword search to social feeds. We’re now entering the next major evolution: the rise of the agentic web.

    What Is the Agentic Web?

    The agentic web is an emerging internet paradigm where intelligent agents—not just humans—are the primary explorers, decision-makers, and even actors on the web.

    These agents might:

    • Research options for a vacation based on a user’s preferences
    • Choose the best product for a specific budget and set of needs
    • Summarize websites and recommend services, often without the user ever clicking through

    They already exist in the form of tools like ChatGPT, Perplexity, Claude, and Google’s Search Generative Experience (SGE). But these tools are quickly evolving from passive assistants to active agents—capable of making decisions, taking actions, and shaping outcomes on behalf of users.

    Why This Shift Matters

    This isn’t just a UX trend—it’s a fundamental change in how visibility and influence work on the internet.

    In the traditional web, discovery happened through search, social, and ads. Brands fought for human attention. The goal was to attract clicks, optimize conversions, and persuade users.

    In the agentic web, brands must first persuade the agents.

    That requires a different kind of presence. Agents don’t respond to visual polish, catchy slogans, or emotional storytelling. They evaluate content based on clarity, structure, specificity, and authority. And their decisions happen in milliseconds—before a human ever gets involved.

    Implications for Content, Design, and Growth

    To thrive in this new environment, businesses and creators need to rethink how they show up online. That includes:

    • Machine-readable clarity: Structured data, transparent claims, and content designed to be easily parsed and summarized.
    • Intent-based experiences: Pages that directly answer questions and match specific goals—rather than generic brand messaging.
    • Reputation signals: Cited sources, consistent updates, and cross-channel presence that AI agents use to evaluate trustworthiness.

    In the agentic web, content isn’t just for people. It’s also for the machines advising them.

    This Is Happening Faster Than You Think

    The shift to agentic discovery isn’t years away—it’s happening now. AI summaries are already replacing links. Product recommendations from bots are already influencing purchases. The design and growth playbooks of the last decade are becoming less effective every month.

    Those who adapt early will benefit from visibility and influence. Those who don’t may struggle to be discovered at all.

    The Bottom Line

    The agentic web is not just another phase of the internet. It’s a reframing of who your audience is, how decisions get made, and what it means to be visible and competitive online.

    We’ve long designed for humans.

    Now, we must design for humans and the agents who serve them.

  • Why Traditional Personalization Is Broken (And What Comes Next)

    Why Traditional Personalization Is Broken (And What Comes Next)

    Marketers and growth teams have been chasing personalization for over a decade—”Hello, {first_name}” emails, geo-targeted ads, product carousels based on past purchases.

    But despite all the effort, most personalization still feels shallow. Worse, it’s often invisible to the user or adds complexity without clear gains.

    So what’s broken—and what’s replacing it?

    The Limits of Conventional Personalization

    Traditional personalization is built on two fragile pillars:

    1. Segmentation: Users are grouped into predefined buckets—like “returning visitor,” “cart abandoner,” or “enterprise lead.” The assumption is that everyone in a segment behaves the same. Spoiler: they don’t.
    2. Static Rules: Personalization logic is hardcoded. Show variation A if they came from Google Ads; show variation B if they’re in New York. It’s rigid, brittle, and impossible to scale beyond a few variables.

    This results in experiences that are “technically personalized” but practically generic.

    The Shift: From Segments to Individuals

    What if websites didn’t rely on a few rules and segments—but learned what each visitor needs in real-time?

    That’s what the new wave of AI-powered personalization enables:

    • Generating content, layout, and messaging on the fly
    • Testing hundreds of variants automatically, not just A/B
    • Adapting continuously based on real-world signals, not assumptions

    Instead of creating “one site for many,” you’re creating one site for one—again and again.

    Why It Matters

    This isn’t just an upgrade. It’s a shift in mindset—and performance.

    • Higher conversions: When the experience reflects a user’s actual intent and mindset, they act.
    • Faster iteration: AI doesn’t need weeks to test ideas; it learns and adapts continuously.
    • Smarter scaling: Instead of building and maintaining 50 variants manually, you let the system evolve automatically as traffic and behavior change.

    The Bottom Line

    We’re moving beyond personalization as a marketing checkbox. The future is about dynamic, intelligent experiences that feel native to every visitor—not because you guessed right, but because your site adapts right.

  • The Rise of Living Websites: The Future of Personalized Digital Experiences

    The Rise of Living Websites: The Future of Personalized Digital Experiences

    The era of static websites is coming to an end.

    For years, digital experiences have remained relatively rigid—designed around fixed templates, updated infrequently, and optimized using broad A/B tests that treat users as segments, not individuals. But the next wave is here: living websites—experiences that evolve in real-time, adapt to individual users, and continuously optimize for outcomes like engagement and conversion.

    What Is a Living Website?

    A living website is dynamic by design. It doesn’t just display content; it reshapes itself based on who is visiting, where they came from, what they’ve done before, and what they’re likely to do next. Think of a homepage that adapts its layout, headlines, and calls to action based on a visitor’s behavior, interests, or even the ad they clicked moments earlier.

    This isn’t science fiction. Advances in AI, real-time data processing, and generative interfaces now make it possible to create websites that respond like a good salesperson: attentive, adaptive, and always iterating.

    The Personalization Challenge

    Personalizing a website for every individual user sounds great in theory—but it’s brutally hard in practice.

    • Most teams lack the bandwidth to manually create and maintain dozens (let alone hundreds) of variants for different audiences.
    • Traditional optimization tools are slow, requiring weeks of testing to decide whether Version A or B performs better—by which time, your audience may have shifted.
    • Data silos and integration headaches make it difficult to act on the rich behavioral signals coming from ads, CRM tools, or product usage data.

    In short, the tools haven’t kept up with the ambition.

    Why It Matters: The Conversion Opportunity

    The payoff for solving these challenges is massive. Personalization isn’t just about user delight—it’s about business outcomes.

    • Visitors who see relevant content convert at higher rates.
    • Bounce rates drop when the experience matches the user’s intent.
    • Acquisition costs decrease when landing pages do more of the heavy lifting.

    In a world where CAC is rising and attention spans are shrinking, optimizing for each visitor—not just each segment—can be the difference between flatlining and compounding growth.

    What Comes Next

    We’re entering a world where website content, structure, and messaging can be generated and optimized in real time—guided by user behavior, performance data, and generative AI. This opens the door to continuously evolving experiences, tailored not just once, but every time a user returns.

    The companies that embrace this shift will stop thinking of their site as a static asset—and start treating it like a living system: always learning, always improving, and always aligned to the user in front of it.